Recent　studies　have　attempted　to　exploit　syntactic　information　(e.g.,　dependency　relation)　to　enhance　Chinese　named　entity　recognition　(NER)　performance　and　achieved　promising　results.　These　methods　usually　leverage　single-grained　syntactic　parsing　results　which　are　based　on　single-grained　word　segmentation.　However,　entities　may　be　annotated　with　varying　granularities,　resulting　in　inconsistent　boundaries　when　compared　to　single-grained　results.　Therefore,　merely　using　single-grained　syntactic　information　may　inadvertently　introduce　noise　into　boundary　detection　in　Chinese　NER.　In　this　paper,　we　introduce　a　Dual-grained　Syntax-aware　Transformer　network　(DuST)　to　mitigate　the　noise　introduced　by　single-grained　syntactic　parsing　results.　We　first　introduce　coarse-　and　fine-grained　syntactic　dependency　parsing　results　to　comprehensively　consider　possible　boundary　scenarios.　We　then　design　the　DuST　network　with　dual　syntax-aware　Transformers　to　capture　syntax-enhanced　features　at　different　granularities,　a　contextual　Transformer　to　model　the　contextual　features　and　an　aggregation　module　to　dynamically　aggregate　these　features.　Experiments　are　conducted　on　four　widely-used　Chinese　NER　datasets　and　our　model　achieves　superior　performance.　Specifically,　our　approach　outperforms　two　single-grained　syntax-enhanced　baselines　with　an　increase　of　up　to　3.9%　and　2.94%　in　F1　score,　respectively.　©　2024　Elsevier　Ltd